Overview

Dataset statistics

Number of variables44
Number of observations20336
Missing cells222441
Missing cells (%)24.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory352.0 B

Variable types

Numeric38
Unsupported1
Categorical5

Alerts

SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Unit1 is highly correlated with Unit2High correlation
Temp has 235 (1.2%) missing values Missing
SBP has 258 (1.3%) missing values Missing
DBP has 7384 (36.3%) missing values Missing
EtCO2 has 20336 (100.0%) missing values Missing
BaseExcess has 7684 (37.8%) missing values Missing
HCO3 has 535 (2.6%) missing values Missing
FiO2 has 8349 (41.1%) missing values Missing
pH has 7155 (35.2%) missing values Missing
PaCO2 has 7759 (38.2%) missing values Missing
SaO2 has 12373 (60.8%) missing values Missing
AST has 14443 (71.0%) missing values Missing
BUN has 427 (2.1%) missing values Missing
Alkalinephos has 14633 (72.0%) missing values Missing
Calcium has 3789 (18.6%) missing values Missing
Chloride has 542 (2.7%) missing values Missing
Creatinine has 461 (2.3%) missing values Missing
Bilirubin_direct has 19750 (97.1%) missing values Missing
Glucose has 407 (2.0%) missing values Missing
Lactate has 12603 (62.0%) missing values Missing
Magnesium has 1388 (6.8%) missing values Missing
Phosphate has 3650 (17.9%) missing values Missing
Potassium has 433 (2.1%) missing values Missing
Bilirubin_total has 14566 (71.6%) missing values Missing
TroponinI has 19847 (97.6%) missing values Missing
Hct has 364 (1.8%) missing values Missing
Hgb has 507 (2.5%) missing values Missing
PTT has 4496 (22.1%) missing values Missing
WBC has 625 (3.1%) missing values Missing
Fibrinogen has 17769 (87.4%) missing values Missing
Platelets has 585 (2.9%) missing values Missing
Unit1 has 9522 (46.8%) missing values Missing
Unit2 has 9522 (46.8%) missing values Missing
O2Sat is highly skewed (γ1 = -21.73522171) Skewed
PatientID has unique values Unique
EtCO2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
BaseExcess has 3101 (15.2%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:22:38.280481
Analysis finished2021-11-29 10:22:47.837749
Duration9.56 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIQUE

Distinct20336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10173.60651
Minimum1
Maximum20643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:47.886940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1017.75
Q15084.75
median10168.5
Q315252.25
95-th percentile19320.25
Maximum20643
Range20642
Interquartile range (IQR)10167.5

Descriptive statistics

Standard deviation5879.461518
Coefficient of variation (CV)0.5779132024
Kurtosis-1.192915145
Mean10173.60651
Median Absolute Deviation (MAD)5084
Skewness0.005160825078
Sum206890462
Variance34568067.75
MonotonicityStrictly increasing
2021-11-29T11:22:47.984968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
135561
 
< 0.1%
135631
 
< 0.1%
135621
 
< 0.1%
135611
 
< 0.1%
135601
 
< 0.1%
135591
 
< 0.1%
135581
 
< 0.1%
135571
 
< 0.1%
135551
 
< 0.1%
Other values (20326)20326
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
206431
< 0.1%
206421
< 0.1%
206411
< 0.1%
206401
< 0.1%
206391
< 0.1%
206381
< 0.1%
206371
< 0.1%
206361
< 0.1%
206351
< 0.1%
206341
< 0.1%

HR
Real number (ℝ≥0)

Distinct256
Distinct (%)1.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean103.0772658
Minimum37
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:48.083661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile75
Q190
median102
Q3115
95-th percentile136
Maximum280
Range243
Interquartile range (IQR)25

Descriptive statistics

Standard deviation18.90264183
Coefficient of variation (CV)0.1833832289
Kurtosis0.9092734871
Mean103.0772658
Median Absolute Deviation (MAD)12
Skewness0.5224319987
Sum2096076.2
Variance357.3098682
MonotonicityNot monotonic
2021-11-29T11:22:48.179643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88485
 
2.4%
90461
 
2.3%
97429
 
2.1%
100428
 
2.1%
96424
 
2.1%
91416
 
2.0%
95406
 
2.0%
99404
 
2.0%
93402
 
2.0%
107395
 
1.9%
Other values (246)16085
79.1%
ValueCountFrequency (%)
371
 
< 0.1%
451
 
< 0.1%
482
 
< 0.1%
501
 
< 0.1%
523
< 0.1%
532
 
< 0.1%
545
< 0.1%
555
< 0.1%
565
< 0.1%
56.51
 
< 0.1%
ValueCountFrequency (%)
2801
< 0.1%
2231
< 0.1%
2101
< 0.1%
2011
< 0.1%
2001
< 0.1%
1991
< 0.1%
1931
< 0.1%
1922
< 0.1%
1891
< 0.1%
1881
< 0.1%

O2Sat
Real number (ℝ≥0)

SKEWED

Distinct35
Distinct (%)0.2%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean99.63535721
Minimum27
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:48.269130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile98
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range73
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.28680395
Coefficient of variation (CV)0.0129151336
Kurtosis925.7564832
Mean99.63535721
Median Absolute Deviation (MAD)0
Skewness-21.73522171
Sum2024989
Variance1.655864405
MonotonicityNot monotonic
2021-11-29T11:22:48.357090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
10016312
80.2%
992030
 
10.0%
981025
 
5.0%
97414
 
2.0%
99.5150
 
0.7%
96143
 
0.7%
98.572
 
0.4%
9548
 
0.2%
97.536
 
0.2%
9421
 
0.1%
Other values (25)73
 
0.4%
ValueCountFrequency (%)
271
 
< 0.1%
361
 
< 0.1%
652
< 0.1%
671
 
< 0.1%
713
< 0.1%
761
 
< 0.1%
792
< 0.1%
821
 
< 0.1%
832
< 0.1%
841
 
< 0.1%
ValueCountFrequency (%)
10016312
80.2%
99.5150
 
0.7%
992030
 
10.0%
98.572
 
0.4%
981025
 
5.0%
97.536
 
0.2%
97414
 
2.0%
96.517
 
0.1%
96143
 
0.7%
95.58
 
< 0.1%

Temp
Real number (ℝ≥0)

MISSING

Distinct249
Distinct (%)1.2%
Missing235
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean37.60381872
Minimum30.5
Maximum42.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:48.456626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30.5
5-th percentile36.56
Q137.11
median37.56
Q338.05
95-th percentile38.89
Maximum42.22
Range11.72
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.7304004107
Coefficient of variation (CV)0.01942357015
Kurtosis1.837707155
Mean37.60381872
Median Absolute Deviation (MAD)0.45
Skewness0.2724133215
Sum755874.36
Variance0.53348476
MonotonicityNot monotonic
2021-11-29T11:22:48.561688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.67690
 
3.4%
37690
 
3.4%
37.17686
 
3.4%
38665
 
3.3%
37.5653
 
3.2%
37.44627
 
3.1%
37.11570
 
2.8%
37.33561
 
2.8%
37.56545
 
2.7%
36.89538
 
2.6%
Other values (239)13876
68.2%
ValueCountFrequency (%)
30.51
 
< 0.1%
32.61
 
< 0.1%
32.71
 
< 0.1%
33.441
 
< 0.1%
33.52
< 0.1%
33.62
< 0.1%
33.72
< 0.1%
34.251
 
< 0.1%
34.281
 
< 0.1%
34.444
< 0.1%
ValueCountFrequency (%)
42.221
< 0.1%
41.61
< 0.1%
41.441
< 0.1%
41.221
< 0.1%
41.171
< 0.1%
41.112
< 0.1%
411
< 0.1%
40.832
< 0.1%
40.781
< 0.1%
40.611
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct392
Distinct (%)2.0%
Missing258
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean147.9312805
Minimum35
Maximum281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:48.660821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile115.5
Q1132
median146
Q3162
95-th percentile188
Maximum281
Range246
Interquartile range (IQR)30

Descriptive statistics

Standard deviation22.38826445
Coefficient of variation (CV)0.1513423285
Kurtosis0.5512952967
Mean147.9312805
Median Absolute Deviation (MAD)15
Skewness0.5090325192
Sum2970164.25
Variance501.234385
MonotonicityNot monotonic
2021-11-29T11:22:48.760082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140383
 
1.9%
148367
 
1.8%
142363
 
1.8%
141360
 
1.8%
144355
 
1.7%
138348
 
1.7%
147343
 
1.7%
136341
 
1.7%
137335
 
1.6%
146334
 
1.6%
Other values (382)16549
81.4%
ValueCountFrequency (%)
351
 
< 0.1%
69.51
 
< 0.1%
721
 
< 0.1%
741
 
< 0.1%
752
< 0.1%
75.51
 
< 0.1%
78.51
 
< 0.1%
811
 
< 0.1%
824
< 0.1%
82.251
 
< 0.1%
ValueCountFrequency (%)
2811
< 0.1%
2741
< 0.1%
272.51
< 0.1%
2721
< 0.1%
2501
< 0.1%
2471
< 0.1%
2461
< 0.1%
2452
< 0.1%
2421
< 0.1%
2411
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct638
Distinct (%)3.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean102.2692102
Minimum22
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:48.858260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile78
Q189
median98.67
Q3110
95-th percentile135
Maximum300
Range278
Interquartile range (IQR)21

Descriptive statistics

Standard deviation22.68689705
Coefficient of variation (CV)0.2218350666
Kurtosis17.90611098
Mean102.2692102
Median Absolute Deviation (MAD)10.66
Skewness3.143988092
Sum2079542.12
Variance514.6952979
MonotonicityNot monotonic
2021-11-29T11:22:48.950725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94425
 
2.1%
96422
 
2.1%
93406
 
2.0%
89402
 
2.0%
98401
 
2.0%
88397
 
2.0%
91395
 
1.9%
92391
 
1.9%
95389
 
1.9%
97385
 
1.9%
Other values (628)16321
80.3%
ValueCountFrequency (%)
221
< 0.1%
49.671
< 0.1%
501
< 0.1%
521
< 0.1%
531
< 0.1%
542
< 0.1%
54.331
< 0.1%
551
< 0.1%
562
< 0.1%
57.51
< 0.1%
ValueCountFrequency (%)
3001
 
< 0.1%
2983
< 0.1%
2972
< 0.1%
2954
< 0.1%
2943
< 0.1%
2931
 
< 0.1%
2921
 
< 0.1%
2912
< 0.1%
2902
< 0.1%
2891
 
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct265
Distinct (%)2.0%
Missing7384
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean78.33315318
Minimum29
Maximum298
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:49.139517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile57
Q167
median75
Q386
95-th percentile109
Maximum298
Range269
Interquartile range (IQR)19

Descriptive statistics

Standard deviation18.07409145
Coefficient of variation (CV)0.2307336129
Kurtosis12.44502426
Mean78.33315318
Median Absolute Deviation (MAD)9
Skewness2.199041031
Sum1014571
Variance326.6727816
MonotonicityNot monotonic
2021-11-29T11:22:49.238500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68376
 
1.8%
70358
 
1.8%
72358
 
1.8%
69358
 
1.8%
73344
 
1.7%
71343
 
1.7%
66333
 
1.6%
74326
 
1.6%
67320
 
1.6%
78317
 
1.6%
Other values (255)9519
46.8%
(Missing)7384
36.3%
ValueCountFrequency (%)
291
< 0.1%
311
< 0.1%
321
< 0.1%
32.751
< 0.1%
341
< 0.1%
361
< 0.1%
36.51
< 0.1%
382
< 0.1%
391
< 0.1%
402
< 0.1%
ValueCountFrequency (%)
2981
< 0.1%
2871
< 0.1%
2721
< 0.1%
2691
< 0.1%
2681
< 0.1%
2671
< 0.1%
2461
< 0.1%
2321
< 0.1%
2221
< 0.1%
2211
< 0.1%

Resp
Real number (ℝ≥0)

Distinct131
Distinct (%)0.6%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.01727152
Minimum9.5
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:49.334732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile19
Q123
median26
Q330
95-th percentile39
Maximum69
Range59.5
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.405685686
Coefficient of variation (CV)0.2370959511
Kurtosis3.699797033
Mean27.01727152
Median Absolute Deviation (MAD)4
Skewness1.3589424
Sum548666.75
Variance41.03280911
MonotonicityNot monotonic
2021-11-29T11:22:49.428676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
241604
 
7.9%
251529
 
7.5%
221394
 
6.9%
261388
 
6.8%
231383
 
6.8%
271264
 
6.2%
281249
 
6.1%
29971
 
4.8%
21960
 
4.7%
20906
 
4.5%
Other values (121)7660
37.7%
ValueCountFrequency (%)
9.51
 
< 0.1%
105
 
< 0.1%
127
 
< 0.1%
133
 
< 0.1%
13.53
 
< 0.1%
13.751
 
< 0.1%
1441
0.2%
14.251
 
< 0.1%
14.58
 
< 0.1%
1537
0.2%
ValueCountFrequency (%)
693
 
< 0.1%
677
< 0.1%
662
 
< 0.1%
65.51
 
< 0.1%
655
< 0.1%
646
< 0.1%
634
 
< 0.1%
621
 
< 0.1%
613
 
< 0.1%
6011
0.1%

EtCO2
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing20336
Missing (%)100.0%
Memory size159.0 KiB

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct75
Distinct (%)0.6%
Missing7684
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean1.436452735
Minimum-25
Maximum100
Zeros3101
Zeros (%)15.2%
Negative2732
Negative (%)13.4%
Memory size159.0 KiB
2021-11-29T11:22:49.528698image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-25
5-th percentile-4
Q10
median1
Q33
95-th percentile8
Maximum100
Range125
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.132239319
Coefficient of variation (CV)2.876697032
Kurtosis31.99474638
Mean1.436452735
Median Absolute Deviation (MAD)2
Skewness1.690405242
Sum18174
Variance17.07540179
MonotonicityNot monotonic
2021-11-29T11:22:49.630720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03101
15.2%
11478
 
7.3%
21232
 
6.1%
31100
 
5.4%
-1852
 
4.2%
4847
 
4.2%
-2598
 
2.9%
5566
 
2.8%
6436
 
2.1%
-3410
 
2.0%
Other values (65)2032
 
10.0%
(Missing)7684
37.8%
ValueCountFrequency (%)
-251
 
< 0.1%
-242
 
< 0.1%
-212
 
< 0.1%
-201
 
< 0.1%
-196
< 0.1%
-18.51
 
< 0.1%
-181
 
< 0.1%
-174
< 0.1%
-168
< 0.1%
-157
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
49.51
 
< 0.1%
441
 
< 0.1%
361
 
< 0.1%
281
 
< 0.1%
262
 
< 0.1%
252
 
< 0.1%
247
< 0.1%
232
 
< 0.1%
224
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)0.3%
Missing535
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean25.60966618
Minimum5
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:49.728595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile20
Q123
median25
Q328
95-th percentile32
Maximum55
Range50
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.008038702
Coefficient of variation (CV)0.1565049179
Kurtosis2.941087155
Mean25.60966618
Median Absolute Deviation (MAD)2
Skewness0.5302548319
Sum507097
Variance16.06437424
MonotonicityNot monotonic
2021-11-29T11:22:49.825394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
252511
12.3%
262350
11.6%
242200
10.8%
272089
10.3%
231840
9.0%
281569
7.7%
221229
 
6.0%
291189
 
5.8%
30780
 
3.8%
21765
 
3.8%
Other values (48)3279
16.1%
ValueCountFrequency (%)
51
 
< 0.1%
61
 
< 0.1%
72
 
< 0.1%
84
 
< 0.1%
95
 
< 0.1%
109
 
< 0.1%
119
 
< 0.1%
1212
0.1%
1323
0.1%
1427
0.1%
ValueCountFrequency (%)
551
 
< 0.1%
521
 
< 0.1%
503
 
< 0.1%
494
 
< 0.1%
482
 
< 0.1%
474
 
< 0.1%
464
 
< 0.1%
459
< 0.1%
4410
< 0.1%
4313
0.1%

FiO2
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)0.5%
Missing8349
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean0.7496262618
Minimum0
Maximum10
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:49.924602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.5
median0.75
Q31
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2671071528
Coefficient of variation (CV)0.3563204311
Kurtosis143.161105
Mean0.7496262618
Median Absolute Deviation (MAD)0.25
Skewness4.350642728
Sum8985.77
Variance0.07134623107
MonotonicityNot monotonic
2021-11-29T11:22:50.021777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15219
25.7%
0.52676
 
13.2%
0.41065
 
5.2%
0.6820
 
4.0%
0.7758
 
3.7%
0.8386
 
1.9%
0.75315
 
1.5%
0.35165
 
0.8%
0.9594
 
0.5%
0.973
 
0.4%
Other values (54)416
 
2.0%
(Missing)8349
41.1%
ValueCountFrequency (%)
02
 
< 0.1%
0.022
 
< 0.1%
0.033
 
< 0.1%
0.044
 
< 0.1%
0.081
 
< 0.1%
0.111
 
< 0.1%
0.230
0.1%
0.2135
0.2%
0.221
 
< 0.1%
0.245
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
71
 
< 0.1%
15219
25.7%
0.999
 
< 0.1%
0.989
 
< 0.1%
0.975
 
< 0.1%
0.964
 
< 0.1%
0.9594
 
0.5%
0.942
 
< 0.1%
0.931
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct80
Distinct (%)0.6%
Missing7155
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean7.426962294
Minimum6.63
Maximum7.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:50.116371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.63
5-th percentile7.33
Q17.39
median7.43
Q37.47
95-th percentile7.52
Maximum7.93
Range1.3
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.06352993288
Coefficient of variation (CV)0.008553959259
Kurtosis7.746723601
Mean7.426962294
Median Absolute Deviation (MAD)0.04
Skewness-0.9298301191
Sum97894.79
Variance0.004036052372
MonotonicityNot monotonic
2021-11-29T11:22:50.218706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.421013
 
5.0%
7.44986
 
4.8%
7.43957
 
4.7%
7.45912
 
4.5%
7.4869
 
4.3%
7.41869
 
4.3%
7.46801
 
3.9%
7.39736
 
3.6%
7.47671
 
3.3%
7.48645
 
3.2%
Other values (70)4722
23.2%
(Missing)7155
35.2%
ValueCountFrequency (%)
6.631
 
< 0.1%
6.651
 
< 0.1%
6.871
 
< 0.1%
6.941
 
< 0.1%
6.983
< 0.1%
72
< 0.1%
7.011
 
< 0.1%
7.021
 
< 0.1%
7.032
< 0.1%
7.051
 
< 0.1%
ValueCountFrequency (%)
7.931
 
< 0.1%
7.81
 
< 0.1%
7.781
 
< 0.1%
7.731
 
< 0.1%
7.721
 
< 0.1%
7.711
 
< 0.1%
7.692
< 0.1%
7.681
 
< 0.1%
7.671
 
< 0.1%
7.663
< 0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct131
Distinct (%)1.0%
Missing7759
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean46.35227002
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:50.321685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile33
Q140
median45
Q350
95-th percentile64
Maximum100
Range90
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.12168536
Coefficient of variation (CV)0.2183643941
Kurtosis4.864624124
Mean46.35227002
Median Absolute Deviation (MAD)5
Skewness1.558116938
Sum582972.5
Variance102.4485144
MonotonicityNot monotonic
2021-11-29T11:22:50.417042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46715
 
3.5%
44686
 
3.4%
42676
 
3.3%
43659
 
3.2%
47620
 
3.0%
45616
 
3.0%
40601
 
3.0%
41567
 
2.8%
48549
 
2.7%
49511
 
2.5%
Other values (121)6377
31.4%
(Missing)7759
38.2%
ValueCountFrequency (%)
101
 
< 0.1%
162
 
< 0.1%
191
 
< 0.1%
208
 
< 0.1%
215
 
< 0.1%
228
 
< 0.1%
2314
0.1%
247
 
< 0.1%
2519
0.1%
2628
0.1%
ValueCountFrequency (%)
10010
< 0.1%
996
< 0.1%
984
 
< 0.1%
97.51
 
< 0.1%
9710
< 0.1%
964
 
< 0.1%
95.51
 
< 0.1%
954
 
< 0.1%
94.51
 
< 0.1%
9412
0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct81
Distinct (%)1.0%
Missing12373
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean95.64866256
Minimum30
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:50.590805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile77
Q197
median98
Q399
95-th percentile99
Maximum100
Range70
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.480275942
Coefficient of variation (CV)0.07820575574
Kurtosis13.82734097
Mean95.64866256
Median Absolute Deviation (MAD)1
Skewness-3.586364209
Sum761650.3
Variance55.95452817
MonotonicityNot monotonic
2021-11-29T11:22:50.688127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
983130
 
15.4%
991929
 
9.5%
971074
 
5.3%
96430
 
2.1%
95195
 
1.0%
100159
 
0.8%
94109
 
0.5%
9367
 
0.3%
9248
 
0.2%
7137
 
0.2%
Other values (71)785
 
3.9%
(Missing)12373
60.8%
ValueCountFrequency (%)
302
 
< 0.1%
341
 
< 0.1%
402
 
< 0.1%
421
 
< 0.1%
431
 
< 0.1%
441
 
< 0.1%
461
 
< 0.1%
495
< 0.1%
502
 
< 0.1%
511
 
< 0.1%
ValueCountFrequency (%)
100159
 
0.8%
99.52
 
< 0.1%
99.31
 
< 0.1%
991929
9.5%
98.530
 
0.1%
983130
15.4%
97.519
 
0.1%
971074
 
5.3%
96.54
 
< 0.1%
96430
 
2.1%

AST
Real number (ℝ≥0)

MISSING

Distinct853
Distinct (%)14.5%
Missing14443
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean246.5676226
Minimum3
Maximum9890
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:50.793080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile14
Q124
median45
Q3107
95-th percentile870.8
Maximum9890
Range9887
Interquartile range (IQR)83

Descriptive statistics

Standard deviation895.2502488
Coefficient of variation (CV)3.630850796
Kurtosis59.17982982
Mean246.5676226
Median Absolute Deviation (MAD)26
Skewness7.255823541
Sum1453023
Variance801473.0079
MonotonicityNot monotonic
2021-11-29T11:22:50.892638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19137
 
0.7%
18127
 
0.6%
16125
 
0.6%
17122
 
0.6%
21120
 
0.6%
20119
 
0.6%
24119
 
0.6%
22117
 
0.6%
23105
 
0.5%
15105
 
0.5%
Other values (843)4697
 
23.1%
(Missing)14443
71.0%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
51
 
< 0.1%
65
 
< 0.1%
75
 
< 0.1%
810
 
< 0.1%
915
 
0.1%
1036
0.2%
1147
0.2%
1257
0.3%
ValueCountFrequency (%)
98901
< 0.1%
98401
< 0.1%
97301
< 0.1%
96401
< 0.1%
95071
< 0.1%
94951
< 0.1%
94901
< 0.1%
94561
< 0.1%
94301
< 0.1%
92481
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct169
Distinct (%)0.8%
Missing427
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean25.02549098
Minimum2
Maximum266
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:50.990370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q113
median19
Q329
95-th percentile66
Maximum266
Range264
Interquartile range (IQR)16

Descriptive statistics

Standard deviation20.14009704
Coefficient of variation (CV)0.8047832929
Kurtosis10.20048738
Mean25.02549098
Median Absolute Deviation (MAD)7
Skewness2.648994795
Sum498232.5
Variance405.6235088
MonotonicityNot monotonic
2021-11-29T11:22:51.089154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14991
 
4.9%
15976
 
4.8%
13953
 
4.7%
16924
 
4.5%
12902
 
4.4%
17881
 
4.3%
11826
 
4.1%
10776
 
3.8%
18758
 
3.7%
19727
 
3.6%
Other values (159)11195
55.1%
ValueCountFrequency (%)
28
 
< 0.1%
337
 
0.2%
481
 
0.4%
5132
 
0.6%
6242
 
1.2%
7358
1.8%
8483
2.4%
9616
3.0%
10776
3.8%
11826
4.1%
ValueCountFrequency (%)
2661
 
< 0.1%
2351
 
< 0.1%
2051
 
< 0.1%
2011
 
< 0.1%
1951
 
< 0.1%
1843
< 0.1%
1741
 
< 0.1%
1711
 
< 0.1%
1702
< 0.1%
1691
 
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct491
Distinct (%)8.6%
Missing14633
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean114.4879888
Minimum7
Maximum3833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:51.188344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile38
Q158
median79
Q3119
95-th percentile297
Maximum3833
Range3826
Interquartile range (IQR)61

Descriptive statistics

Standard deviation140.5028521
Coefficient of variation (CV)1.227227883
Kurtosis150.3540625
Mean114.4879888
Median Absolute Deviation (MAD)26
Skewness9.156134551
Sum652925
Variance19741.05146
MonotonicityNot monotonic
2021-11-29T11:22:51.288702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5592
 
0.5%
6080
 
0.4%
5980
 
0.4%
6779
 
0.4%
4977
 
0.4%
7375
 
0.4%
7275
 
0.4%
5874
 
0.4%
6974
 
0.4%
6172
 
0.4%
Other values (481)4925
 
24.2%
(Missing)14633
72.0%
ValueCountFrequency (%)
71
 
< 0.1%
121
 
< 0.1%
151
 
< 0.1%
171
 
< 0.1%
181
 
< 0.1%
191
 
< 0.1%
202
< 0.1%
211
 
< 0.1%
223
< 0.1%
234
< 0.1%
ValueCountFrequency (%)
38331
< 0.1%
25281
< 0.1%
24401
< 0.1%
21901
< 0.1%
21211
< 0.1%
17991
< 0.1%
16691
< 0.1%
15461
< 0.1%
15011
< 0.1%
14371
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct109
Distinct (%)0.7%
Missing3789
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean8.549410769
Minimum3.9
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:51.394596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.9
5-th percentile7.5
Q18.1
median8.5
Q38.9
95-th percentile9.7
Maximum22
Range18.1
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.7765680555
Coefficient of variation (CV)0.09083293299
Kurtosis22.34361276
Mean8.549410769
Median Absolute Deviation (MAD)0.4
Skewness2.087220841
Sum141467.1
Variance0.6030579448
MonotonicityNot monotonic
2021-11-29T11:22:51.495690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.51107
 
5.4%
8.61054
 
5.2%
8.41032
 
5.1%
8.31018
 
5.0%
8.2975
 
4.8%
8.7961
 
4.7%
8.8922
 
4.5%
8.1859
 
4.2%
8.9799
 
3.9%
9747
 
3.7%
Other values (99)7073
34.8%
(Missing)3789
18.6%
ValueCountFrequency (%)
3.91
 
< 0.1%
4.51
 
< 0.1%
4.72
< 0.1%
5.33
< 0.1%
5.41
 
< 0.1%
5.51
 
< 0.1%
5.62
< 0.1%
5.71
 
< 0.1%
5.82
< 0.1%
5.91
 
< 0.1%
ValueCountFrequency (%)
221
< 0.1%
21.51
< 0.1%
19.61
< 0.1%
19.21
< 0.1%
171
< 0.1%
16.21
< 0.1%
15.71
< 0.1%
15.61
< 0.1%
15.42
< 0.1%
15.31
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct65
Distinct (%)0.3%
Missing542
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean107.1987724
Minimum73
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:51.604846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile98
Q1104
median107
Q3111
95-th percentile116
Maximum145
Range72
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.611802707
Coefficient of variation (CV)0.05234950535
Kurtosis1.798068267
Mean107.1987724
Median Absolute Deviation (MAD)3
Skewness0.07079333277
Sum2121892.5
Variance31.49232962
MonotonicityNot monotonic
2021-11-29T11:22:51.702637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1081599
 
7.9%
1071569
 
7.7%
1091501
 
7.4%
1061487
 
7.3%
1101390
 
6.8%
1051333
 
6.6%
1041185
 
5.8%
1111162
 
5.7%
103994
 
4.9%
112967
 
4.8%
Other values (55)6607
32.5%
ValueCountFrequency (%)
731
 
< 0.1%
741
 
< 0.1%
802
 
< 0.1%
811
 
< 0.1%
821
 
< 0.1%
834
 
< 0.1%
841
 
< 0.1%
852
 
< 0.1%
865
< 0.1%
8711
0.1%
ValueCountFrequency (%)
1451
 
< 0.1%
1411
 
< 0.1%
1402
 
< 0.1%
1394
< 0.1%
1376
< 0.1%
1354
< 0.1%
1341
 
< 0.1%
1331
 
< 0.1%
1325
< 0.1%
1318
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct156
Distinct (%)0.8%
Missing461
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1.416528302
Minimum0.1
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:51.806123image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q10.7
median0.9
Q31.3
95-th percentile4.4
Maximum46.6
Range46.5
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.60387656
Coefficient of variation (CV)1.132258747
Kurtosis55.75782003
Mean1.416528302
Median Absolute Deviation (MAD)0.2
Skewness5.189413788
Sum28153.5
Variance2.572420019
MonotonicityNot monotonic
2021-11-29T11:22:51.902051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.82498
12.3%
0.72343
11.5%
0.92179
10.7%
11765
 
8.7%
0.61743
 
8.6%
1.11298
 
6.4%
0.5995
 
4.9%
1.2924
 
4.5%
1.3775
 
3.8%
1.4557
 
2.7%
Other values (146)4798
23.6%
(Missing)461
 
2.3%
ValueCountFrequency (%)
0.14
 
< 0.1%
0.220
 
0.1%
0.386
 
0.4%
0.4336
 
1.7%
0.5995
 
4.9%
0.551
 
< 0.1%
0.61743
8.6%
0.72343
11.5%
0.82498
12.3%
0.851
 
< 0.1%
ValueCountFrequency (%)
46.61
< 0.1%
29.11
< 0.1%
21.41
< 0.1%
19.91
< 0.1%
18.81
< 0.1%
18.51
< 0.1%
17.61
< 0.1%
17.31
< 0.1%
171
< 0.1%
161
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct112
Distinct (%)19.1%
Missing19750
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean2.905631399
Minimum0.1
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:52.075984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median1.1
Q33.3
95-th percentile11.8
Maximum37.5
Range37.4
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation4.739387861
Coefficient of variation (CV)1.631104297
Kurtosis15.48354135
Mean2.905631399
Median Absolute Deviation (MAD)0.9
Skewness3.501304236
Sum1702.7
Variance22.46179729
MonotonicityNot monotonic
2021-11-29T11:22:52.172118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.253
 
0.3%
0.143
 
0.2%
0.440
 
0.2%
0.331
 
0.2%
0.526
 
0.1%
0.626
 
0.1%
0.720
 
0.1%
0.819
 
0.1%
1.119
 
0.1%
118
 
0.1%
Other values (102)291
 
1.4%
(Missing)19750
97.1%
ValueCountFrequency (%)
0.143
0.2%
0.253
0.3%
0.331
0.2%
0.440
0.2%
0.526
0.1%
0.626
0.1%
0.720
 
0.1%
0.819
 
0.1%
0.99
 
< 0.1%
118
 
0.1%
ValueCountFrequency (%)
37.51
< 0.1%
351
< 0.1%
301
< 0.1%
29.11
< 0.1%
281
< 0.1%
26.41
< 0.1%
22.21
< 0.1%
21.61
< 0.1%
21.21
< 0.1%
211
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct676
Distinct (%)3.4%
Missing407
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean163.8167745
Minimum19
Maximum988
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:52.276117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile93
Q1123
median149
Q3184
95-th percentile280
Maximum988
Range969
Interquartile range (IQR)61

Descriptive statistics

Standard deviation70.79349599
Coefficient of variation (CV)0.4321504692
Kurtosis21.57353284
Mean163.8167745
Median Absolute Deviation (MAD)30
Skewness3.39599843
Sum3264704.5
Variance5011.719074
MonotonicityNot monotonic
2021-11-29T11:22:52.376894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142207
 
1.0%
145195
 
1.0%
124195
 
1.0%
129195
 
1.0%
136194
 
1.0%
144194
 
1.0%
149193
 
0.9%
131193
 
0.9%
134193
 
0.9%
127191
 
0.9%
Other values (666)17979
88.4%
(Missing)407
 
2.0%
ValueCountFrequency (%)
191
< 0.1%
311
< 0.1%
382
< 0.1%
401
< 0.1%
411
< 0.1%
421
< 0.1%
461
< 0.1%
471
< 0.1%
481
< 0.1%
512
< 0.1%
ValueCountFrequency (%)
9881
< 0.1%
9601
< 0.1%
9521
< 0.1%
9341
< 0.1%
9241
< 0.1%
9141
< 0.1%
9131
< 0.1%
9121
< 0.1%
9071
< 0.1%
8961
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct248
Distinct (%)3.2%
Missing12603
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean2.607383939
Minimum0.3
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:52.477806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.8
Q11.3
median1.9
Q33
95-th percentile6.6
Maximum31
Range30.7
Interquartile range (IQR)1.7

Descriptive statistics

Standard deviation2.392212533
Coefficient of variation (CV)0.9174761329
Kurtosis24.48338048
Mean2.607383939
Median Absolute Deviation (MAD)0.7
Skewness4.019000215
Sum20162.9
Variance5.722680804
MonotonicityNot monotonic
2021-11-29T11:22:52.575299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3378
 
1.9%
1.4367
 
1.8%
1.2357
 
1.8%
1350
 
1.7%
1.6348
 
1.7%
1.5329
 
1.6%
1.1302
 
1.5%
1.7292
 
1.4%
1.8288
 
1.4%
0.9270
 
1.3%
Other values (238)4452
 
21.9%
(Missing)12603
62.0%
ValueCountFrequency (%)
0.32
 
< 0.1%
0.371
 
< 0.1%
0.45
 
< 0.1%
0.518
 
0.1%
0.551
 
< 0.1%
0.656
 
0.3%
0.7114
0.6%
0.731
 
< 0.1%
0.8210
1.0%
0.9270
1.3%
ValueCountFrequency (%)
311
< 0.1%
28.91
< 0.1%
28.81
< 0.1%
271
< 0.1%
25.91
< 0.1%
24.61
< 0.1%
24.51
< 0.1%
241
< 0.1%
23.51
< 0.1%
23.31
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct63
Distinct (%)0.3%
Missing1388
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean2.180462318
Minimum0.8
Maximum9.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:52.679885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.6
Q11.9
median2.1
Q32.4
95-th percentile2.9
Maximum9.7
Range8.9
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.4214289648
Coefficient of variation (CV)0.1932750506
Kurtosis31.062933
Mean2.180462318
Median Absolute Deviation (MAD)0.2
Skewness2.896517979
Sum41315.4
Variance0.1776023724
MonotonicityNot monotonic
2021-11-29T11:22:52.772785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.12458
12.1%
22321
11.4%
2.22220
10.9%
1.91857
9.1%
2.31809
8.9%
2.41408
6.9%
1.81357
6.7%
2.51009
 
5.0%
1.7895
 
4.4%
2.6735
 
3.6%
Other values (53)2879
14.2%
(Missing)1388
6.8%
ValueCountFrequency (%)
0.81
 
< 0.1%
0.92
 
< 0.1%
18
 
< 0.1%
1.112
 
0.1%
1.228
 
0.1%
1.373
 
0.4%
1.4122
 
0.6%
1.5234
 
1.2%
1.6535
2.6%
1.7895
4.4%
ValueCountFrequency (%)
9.71
 
< 0.1%
9.61
 
< 0.1%
8.92
< 0.1%
8.31
 
< 0.1%
8.21
 
< 0.1%
7.61
 
< 0.1%
7.51
 
< 0.1%
6.51
 
< 0.1%
6.41
 
< 0.1%
6.33
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct138
Distinct (%)0.8%
Missing3650
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean3.925970874
Minimum0.5
Maximum18.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:52.868981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile2.3
Q13.1
median3.7
Q34.4
95-th percentile6.4
Maximum18.8
Range18.3
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.376467273
Coefficient of variation (CV)0.3506055743
Kurtosis8.827872226
Mean3.925970874
Median Absolute Deviation (MAD)0.7
Skewness2.076309712
Sum65508.75
Variance1.894662153
MonotonicityNot monotonic
2021-11-29T11:22:52.964406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4731
 
3.6%
3.6712
 
3.5%
3.7706
 
3.5%
3.2702
 
3.5%
3.5700
 
3.4%
3.3687
 
3.4%
3.1652
 
3.2%
3.8647
 
3.2%
3.9640
 
3.1%
4.1572
 
2.8%
Other values (128)9937
48.9%
(Missing)3650
 
17.9%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.71
 
< 0.1%
0.81
 
< 0.1%
0.92
 
< 0.1%
15
 
< 0.1%
1.16
 
< 0.1%
1.216
0.1%
1.315
0.1%
1.423
0.1%
1.525
0.1%
ValueCountFrequency (%)
18.81
< 0.1%
17.61
< 0.1%
16.91
< 0.1%
16.51
< 0.1%
16.41
< 0.1%
15.61
< 0.1%
14.51
< 0.1%
14.21
< 0.1%
14.11
< 0.1%
141
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct102
Distinct (%)0.5%
Missing433
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean4.480864191
Minimum2.2
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:53.064622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile3.6
Q14
median4.4
Q34.8
95-th percentile5.7
Maximum27.5
Range25.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.719283605
Coefficient of variation (CV)0.1605234112
Kurtosis59.32436812
Mean4.480864191
Median Absolute Deviation (MAD)0.4
Skewness3.297248628
Sum89182.64
Variance0.5173689044
MonotonicityNot monotonic
2021-11-29T11:22:53.162156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.21529
 
7.5%
4.31525
 
7.5%
4.41424
 
7.0%
4.11417
 
7.0%
41336
 
6.6%
4.51336
 
6.6%
4.61187
 
5.8%
3.91078
 
5.3%
4.71024
 
5.0%
4.8872
 
4.3%
Other values (92)7175
35.3%
ValueCountFrequency (%)
2.21
 
< 0.1%
2.51
 
< 0.1%
2.72
 
< 0.1%
2.87
 
< 0.1%
2.93
 
< 0.1%
332
 
0.2%
3.149
 
0.2%
3.280
0.4%
3.3129
0.6%
3.351
 
< 0.1%
ValueCountFrequency (%)
27.51
 
< 0.1%
131
 
< 0.1%
103
< 0.1%
9.93
< 0.1%
9.83
< 0.1%
9.73
< 0.1%
9.52
< 0.1%
9.42
< 0.1%
9.31
 
< 0.1%
9.24
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct240
Distinct (%)4.2%
Missing14566
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean1.9555026
Minimum0.1
Maximum46.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:53.258650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.5
95-th percentile7.8
Maximum46.6
Range46.5
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation4.244483696
Coefficient of variation (CV)2.170533395
Kurtosis39.29864828
Mean1.9555026
Median Absolute Deviation (MAD)0.4
Skewness5.637871326
Sum11283.25
Variance18.01564184
MonotonicityNot monotonic
2021-11-29T11:22:53.355742image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3549
 
2.7%
0.5546
 
2.7%
0.4538
 
2.6%
0.6457
 
2.2%
0.7420
 
2.1%
0.8348
 
1.7%
0.2325
 
1.6%
0.9274
 
1.3%
1218
 
1.1%
1.1182
 
0.9%
Other values (230)1913
 
9.4%
(Missing)14566
71.6%
ValueCountFrequency (%)
0.159
 
0.3%
0.2325
1.6%
0.3549
2.7%
0.4538
2.6%
0.5546
2.7%
0.6457
2.2%
0.7420
2.1%
0.8348
1.7%
0.9274
1.3%
1218
 
1.1%
ValueCountFrequency (%)
46.61
< 0.1%
46.51
< 0.1%
45.91
< 0.1%
44.61
< 0.1%
44.31
< 0.1%
44.11
< 0.1%
43.71
< 0.1%
43.21
< 0.1%
42.41
< 0.1%
40.91
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct214
Distinct (%)43.8%
Missing19847
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean10.43271984
Minimum0.3
Maximum49.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:53.529778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.4
Q10.9
median4.2
Q315.4
95-th percentile41.3
Maximum49.3
Range49
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation12.94786966
Coefficient of variation (CV)1.24108285
Kurtosis1.077420411
Mean10.43271984
Median Absolute Deviation (MAD)3.7
Skewness1.445003395
Sum5101.6
Variance167.6473289
MonotonicityNot monotonic
2021-11-29T11:22:53.631941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.522
 
0.1%
0.322
 
0.1%
0.821
 
0.1%
0.421
 
0.1%
0.617
 
0.1%
0.715
 
0.1%
0.911
 
0.1%
111
 
0.1%
1.18
 
< 0.1%
1.28
 
< 0.1%
Other values (204)333
 
1.6%
(Missing)19847
97.6%
ValueCountFrequency (%)
0.322
0.1%
0.421
0.1%
0.522
0.1%
0.617
0.1%
0.715
0.1%
0.821
0.1%
0.911
0.1%
111
0.1%
1.18
 
< 0.1%
1.28
 
< 0.1%
ValueCountFrequency (%)
49.31
< 0.1%
491
< 0.1%
48.71
< 0.1%
48.52
< 0.1%
482
< 0.1%
47.11
< 0.1%
46.51
< 0.1%
461
< 0.1%
45.21
< 0.1%
45.11
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct407
Distinct (%)2.0%
Missing364
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.67926747
Minimum11
Maximum71.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:53.730942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile26.8
Q130.4
median33.2
Q336.5
95-th percentile42
Maximum71.7
Range60.7
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation4.711327099
Coefficient of variation (CV)0.1398880514
Kurtosis1.049059462
Mean33.67926747
Median Absolute Deviation (MAD)3
Skewness0.5834176548
Sum672642.33
Variance22.19660303
MonotonicityNot monotonic
2021-11-29T11:22:53.822723image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32370
 
1.8%
34315
 
1.5%
35301
 
1.5%
33300
 
1.5%
31269
 
1.3%
36259
 
1.3%
30243
 
1.2%
37229
 
1.1%
38224
 
1.1%
29206
 
1.0%
Other values (397)17256
84.9%
(Missing)364
 
1.8%
ValueCountFrequency (%)
111
< 0.1%
12.51
< 0.1%
15.81
< 0.1%
16.81
< 0.1%
18.41
< 0.1%
18.91
< 0.1%
19.11
< 0.1%
19.52
< 0.1%
19.61
< 0.1%
19.81
< 0.1%
ValueCountFrequency (%)
71.71
< 0.1%
64.61
< 0.1%
61.81
< 0.1%
61.71
< 0.1%
611
< 0.1%
60.51
< 0.1%
58.81
< 0.1%
581
< 0.1%
56.41
< 0.1%
56.32
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct184
Distinct (%)0.9%
Missing507
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean11.34229311
Minimum3.5
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:53.920523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile8.8
Q110.2
median11.2
Q312.4
95-th percentile14.4
Maximum32
Range28.5
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation1.700859441
Coefficient of variation (CV)0.1499572816
Kurtosis1.527280706
Mean11.34229311
Median Absolute Deviation (MAD)1.1
Skewness0.5419012209
Sum224906.33
Variance2.892922838
MonotonicityNot monotonic
2021-11-29T11:22:54.012961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11522
 
2.6%
10.7505
 
2.5%
11.1504
 
2.5%
10.9496
 
2.4%
11.3495
 
2.4%
10.5493
 
2.4%
11.2484
 
2.4%
10.6482
 
2.4%
10.8465
 
2.3%
11.4462
 
2.3%
Other values (174)14921
73.4%
(Missing)507
 
2.5%
ValueCountFrequency (%)
3.51
< 0.1%
4.41
< 0.1%
5.41
< 0.1%
5.61
< 0.1%
5.71
< 0.1%
61
< 0.1%
6.21
< 0.1%
6.32
< 0.1%
6.42
< 0.1%
6.52
< 0.1%
ValueCountFrequency (%)
321
< 0.1%
22.11
< 0.1%
20.31
< 0.1%
19.61
< 0.1%
19.51
< 0.1%
19.41
< 0.1%
19.31
< 0.1%
18.81
< 0.1%
18.71
< 0.1%
18.61
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct1136
Distinct (%)7.2%
Missing4496
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean42.74270896
Minimum17.1
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:54.111001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum17.1
5-th percentile23.4
Q127.7
median32.4
Q343
95-th percentile114.905
Maximum150
Range132.9
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation28.25780232
Coefficient of variation (CV)0.6611139772
Kurtosis6.122138596
Mean42.74270896
Median Absolute Deviation (MAD)6
Skewness2.545713447
Sum677044.51
Variance798.5033919
MonotonicityNot monotonic
2021-11-29T11:22:54.211438image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150511
 
2.5%
27.7112
 
0.6%
27.6106
 
0.5%
28.1105
 
0.5%
29.3104
 
0.5%
28.6103
 
0.5%
28.2101
 
0.5%
29.2100
 
0.5%
28.8100
 
0.5%
2799
 
0.5%
Other values (1126)14399
70.8%
(Missing)4496
 
22.1%
ValueCountFrequency (%)
17.11
 
< 0.1%
17.21
 
< 0.1%
17.31
 
< 0.1%
18.11
 
< 0.1%
18.21
 
< 0.1%
18.42
< 0.1%
18.53
< 0.1%
18.61
 
< 0.1%
18.74
< 0.1%
18.83
< 0.1%
ValueCountFrequency (%)
150511
2.5%
149.91
 
< 0.1%
149.81
 
< 0.1%
148.91
 
< 0.1%
148.82
 
< 0.1%
148.71
 
< 0.1%
148.31
 
< 0.1%
148.21
 
< 0.1%
1481
 
< 0.1%
147.71
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct532
Distinct (%)2.7%
Missing625
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean13.24228705
Minimum0.1
Maximum422.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:54.312275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.5
Q19
median12.1
Q315.9
95-th percentile24.2
Maximum422.9
Range422.8
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation7.949554985
Coefficient of variation (CV)0.6003158636
Kurtosis445.6163799
Mean13.24228705
Median Absolute Deviation (MAD)3.4
Skewness12.18112024
Sum261018.72
Variance63.19542446
MonotonicityNot monotonic
2021-11-29T11:22:54.406671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.6195
 
1.0%
11.5186
 
0.9%
10.7186
 
0.9%
9.5180
 
0.9%
9.8180
 
0.9%
11.2176
 
0.9%
10.2173
 
0.9%
10.3173
 
0.9%
11.8172
 
0.8%
10.4168
 
0.8%
Other values (522)17922
88.1%
(Missing)625
 
3.1%
ValueCountFrequency (%)
0.16
< 0.1%
0.210
< 0.1%
0.36
< 0.1%
0.42
 
< 0.1%
0.52
 
< 0.1%
0.63
 
< 0.1%
0.74
 
< 0.1%
0.83
 
< 0.1%
0.91
 
< 0.1%
11
 
< 0.1%
ValueCountFrequency (%)
422.91
< 0.1%
224.91
< 0.1%
222.81
< 0.1%
170.31
< 0.1%
168.61
< 0.1%
137.81
< 0.1%
128.71
< 0.1%
126.21
< 0.1%
125.71
< 0.1%
123.11
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct650
Distinct (%)25.3%
Missing17769
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean330.9836774
Minimum52.5
Maximum1760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:54.504780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum52.5
5-th percentile133.3
Q1203
median286
Q3416
95-th percentile676
Maximum1760
Range1707.5
Interquartile range (IQR)213

Descriptive statistics

Standard deviation175.8937644
Coefficient of variation (CV)0.5314273071
Kurtosis3.366347057
Mean330.9836774
Median Absolute Deviation (MAD)96
Skewness1.434675366
Sum849635.1
Variance30938.61635
MonotonicityNot monotonic
2021-11-29T11:22:54.602779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21417
 
0.1%
18515
 
0.1%
21615
 
0.1%
21014
 
0.1%
23314
 
0.1%
18313
 
0.1%
28013
 
0.1%
21712
 
0.1%
20312
 
0.1%
21512
 
0.1%
Other values (640)2430
 
11.9%
(Missing)17769
87.4%
ValueCountFrequency (%)
52.51
< 0.1%
581
< 0.1%
631
< 0.1%
651
< 0.1%
762
< 0.1%
811
< 0.1%
821
< 0.1%
852
< 0.1%
871
< 0.1%
881
< 0.1%
ValueCountFrequency (%)
17601
< 0.1%
13831
< 0.1%
12461
< 0.1%
11611
< 0.1%
10761
< 0.1%
10302
< 0.1%
9941
< 0.1%
9791
< 0.1%
9761
< 0.1%
9601
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct753
Distinct (%)3.8%
Missing585
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean229.3742595
Minimum10
Maximum1783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:54.705704image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile98
Q1158
median208
Q3275
95-th percentile430.5
Maximum1783
Range1773
Interquartile range (IQR)117

Descriptive statistics

Standard deviation111.6872719
Coefficient of variation (CV)0.4869215584
Kurtosis11.07361937
Mean229.3742595
Median Absolute Deviation (MAD)57
Skewness2.14067279
Sum4530371
Variance12474.04671
MonotonicityNot monotonic
2021-11-29T11:22:54.803712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167113
 
0.6%
186113
 
0.6%
172112
 
0.6%
197112
 
0.6%
155110
 
0.5%
192109
 
0.5%
198108
 
0.5%
187108
 
0.5%
188108
 
0.5%
207107
 
0.5%
Other values (743)18651
91.7%
(Missing)585
 
2.9%
ValueCountFrequency (%)
101
 
< 0.1%
131
 
< 0.1%
142
< 0.1%
162
< 0.1%
171
 
< 0.1%
183
< 0.1%
192
< 0.1%
201
 
< 0.1%
212
< 0.1%
223
< 0.1%
ValueCountFrequency (%)
17831
< 0.1%
16671
< 0.1%
13431
< 0.1%
13391
< 0.1%
12742
< 0.1%
12531
< 0.1%
12011
< 0.1%
11971
< 0.1%
11292
< 0.1%
11111
< 0.1%

Age
Real number (ℝ≥0)

Distinct5971
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.6216129
Minimum18.11
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:54.985922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.11
5-th percentile30.8475
Q152.29
median64.67
Q375.66
95-th percentile84.95
Maximum89
Range70.89
Interquartile range (IQR)23.37

Descriptive statistics

Standard deviation16.23615352
Coefficient of variation (CV)0.2592739594
Kurtosis-0.2520549698
Mean62.6216129
Median Absolute Deviation (MAD)11.61
Skewness-0.5902903968
Sum1273473.12
Variance263.6126812
MonotonicityNot monotonic
2021-11-29T11:22:55.085091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.8213
 
0.1%
69.6812
 
0.1%
61.0812
 
0.1%
68.1712
 
0.1%
65.4712
 
0.1%
71.3712
 
0.1%
68.3711
 
0.1%
69.5811
 
0.1%
78.4211
 
0.1%
60.8811
 
0.1%
Other values (5961)20219
99.4%
ValueCountFrequency (%)
18.113
< 0.1%
18.131
 
< 0.1%
18.142
< 0.1%
18.151
 
< 0.1%
18.181
 
< 0.1%
18.241
 
< 0.1%
18.321
 
< 0.1%
18.341
 
< 0.1%
18.352
< 0.1%
18.361
 
< 0.1%
ValueCountFrequency (%)
891
 
< 0.1%
88.991
 
< 0.1%
88.982
 
< 0.1%
88.974
< 0.1%
88.961
 
< 0.1%
88.954
< 0.1%
88.942
 
< 0.1%
88.931
 
< 0.1%
88.925
< 0.1%
88.94
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
1
11834 
0
8502 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Length

2021-11-29T11:22:55.182960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:22:55.235961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring characters

ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
0.0
5470 
1.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05470
26.9%
1.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:22:55.287546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:22:55.336660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.05470
50.6%
1.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016284
75.3%
15344
 
24.7%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
1.0
5470 
0.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05470
26.9%
0.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:22:55.388775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:22:55.437846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.05470
50.6%
0.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016158
74.7%
15470
 
25.3%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

HospAdmTime
Real number (ℝ)

Distinct7152
Distinct (%)35.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-48.67841062
Minimum-3710.66
Maximum23.99
Zeros168
Zeros (%)0.8%
Negative19912
Negative (%)97.9%
Memory size159.0 KiB
2021-11-29T11:22:55.500690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3710.66
5-th percentile-236.12
Q1-34.135
median-2.77
Q3-0.02
95-th percentile-0.01
Maximum23.99
Range3734.65
Interquartile range (IQR)34.115

Descriptive statistics

Standard deviation143.6833182
Coefficient of variation (CV)-2.951684666
Kurtosis123.0885825
Mean-48.67841062
Median Absolute Deviation (MAD)2.75
Skewness-8.542504055
Sum-989875.48
Variance20644.89593
MonotonicityNot monotonic
2021-11-29T11:22:55.600761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.023749
 
18.4%
-0.032290
 
11.3%
-0.011114
 
5.5%
-0.04658
 
3.2%
-0.05314
 
1.5%
0168
 
0.8%
-0.06136
 
0.7%
-0.0782
 
0.4%
-0.0840
 
0.2%
-0.0932
 
0.2%
Other values (7142)11752
57.8%
ValueCountFrequency (%)
-3710.661
< 0.1%
-3322.91
< 0.1%
-3269.11
< 0.1%
-3212.561
< 0.1%
-3141.551
< 0.1%
-2668.771
< 0.1%
-2562.531
< 0.1%
-2506.691
< 0.1%
-2476.581
< 0.1%
-2379.761
< 0.1%
ValueCountFrequency (%)
23.991
< 0.1%
22.041
< 0.1%
20.041
< 0.1%
17.341
< 0.1%
16.021
< 0.1%
14.651
< 0.1%
14.211
< 0.1%
141
< 0.1%
11.941
< 0.1%
10.991
< 0.1%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct231
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.77419355
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:55.708439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile16
Q126
median40
Q348
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.5524824
Coefficient of variation (CV)0.5670129396
Kurtosis42.39177693
Mean39.77419355
Median Absolute Deviation (MAD)11
Skewness4.846038135
Sum808848
Variance508.6144626
MonotonicityNot monotonic
2021-11-29T11:22:55.808179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36693
 
3.4%
41655
 
3.2%
38651
 
3.2%
39637
 
3.1%
42629
 
3.1%
43628
 
3.1%
40621
 
3.1%
37614
 
3.0%
46604
 
3.0%
44583
 
2.9%
Other values (221)14021
68.9%
ValueCountFrequency (%)
888
 
0.4%
9101
 
0.5%
1086
 
0.4%
1196
 
0.5%
1296
 
0.5%
13123
0.6%
14155
0.8%
15187
0.9%
16222
1.1%
17282
1.4%
ValueCountFrequency (%)
3369
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3061
 
< 0.1%
3051
 
< 0.1%
3031
 
< 0.1%
2971
 
< 0.1%
2861
 
< 0.1%
2831
 
< 0.1%
2791
 
< 0.1%

SepsisLabel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
0
18546 
1
 
1790

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Length

2021-11-29T11:22:55.906553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:22:55.959456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring characters

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
0
18546 
1
 
1790

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Length

2021-11-29T11:22:56.014469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:22:56.067818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring characters

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.85793666
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:22:56.129846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q125
median39
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.30865867
Coefficient of variation (CV)0.5741081639
Kurtosis40.49091179
Mean38.85793666
Median Absolute Deviation (MAD)11
Skewness4.703211094
Sum790215
Variance497.6762517
MonotonicityNot monotonic
2021-11-29T11:22:56.230375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36700
 
3.4%
39665
 
3.3%
38656
 
3.2%
40634
 
3.1%
41632
 
3.1%
37632
 
3.1%
43600
 
3.0%
42597
 
2.9%
44580
 
2.9%
46560
 
2.8%
Other values (218)14080
69.2%
ValueCountFrequency (%)
8124
 
0.6%
9122
 
0.6%
1095
 
0.5%
11114
 
0.6%
12121
 
0.6%
13145
0.7%
14194
1.0%
15231
1.1%
16274
1.3%
17348
1.7%
ValueCountFrequency (%)
3365
< 0.1%
3351
 
< 0.1%
3341
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3052
 
< 0.1%
2971
 
< 0.1%
2861
 
< 0.1%
2791
 
< 0.1%
2771
 
< 0.1%

Interactions

2021-11-29T11:22:44.894444image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.458821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.548432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.634301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.726476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.815990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.901719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:41.988703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.077448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.168046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.258562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.343054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.428580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.591892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.676598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.766360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.854849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:42.940577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.036917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.131364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.224583image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.308640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.399785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.486817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.575932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.661198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.755118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.839605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:43.924424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.008194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.097244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.183553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.272929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.356361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.449440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.616334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.706975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:22:44.802073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:22:56.451317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:22:56.795624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:22:57.144891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:22:57.431922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:22:45.139453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:22:46.209550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:22:46.921449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:22:47.658647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
01117.0100.037.44181.0141.33NaN32.0NaN24.048.00.37.40100.091.016.022.098.09.685.00.7NaN193.0NaN2.23.74.60.3NaN37.212.5NaN14.7NaN338.083.140NaNNaN-0.03540054
1294.0100.036.44194.0116.0066.027.0NaNNaN22.0NaNNaNNaNNaNNaN100.0NaN7.9113.02.5NaN78.0NaN2.54.45.1NaNNaN27.89.7NaN11.0NaN158.075.9100.01.0-98.60230023
2393.099.038.61159.099.0069.040.0NaN8.032.00.87.5141.0NaNNaN31.0NaN11.1100.00.9NaN130.0NaN2.52.94.1NaNNaN32.111.030.510.0NaN488.045.8201.00.0-1195.71480048
34113.0100.036.78132.584.0061.526.0NaN0.022.0NaN7.4145.098.0NaN19.0NaN8.2108.00.8NaN253.0NaN2.43.85.0NaNNaN27.68.322.37.6NaN220.065.7100.01.0-8.77290029
4588.099.037.33150.0103.00NaN21.0NaNNaN28.0NaNNaNNaNNaN30.09.080.08.5106.00.7NaN138.0NaN2.53.04.00.6NaN45.715.529.08.1NaN288.028.0911.00.0-0.05490048
56111.0100.036.72150.0100.00NaN43.0NaN0.029.00.47.3447.0NaNNaN9.0NaNNaN111.00.7NaN293.01.4NaNNaN3.8NaNNaN36.912.2NaN12.0NaN298.052.0111.00.0-0.03190017
67155.5100.038.39147.5102.0082.033.0NaN-6.020.01.07.4036.0NaN452.071.088.08.0123.03.9NaN263.02.21.93.84.61.4NaN46.016.427.19.7NaN66.064.2411.00.0-0.05450045
7888.0100.036.89136.081.0056.022.0NaN-6.017.0NaN7.3737.0NaNNaN31.0NaN8.2110.01.3NaN129.02.12.53.85.7NaNNaN32.911.4NaN11.4NaN357.087.081NaNNaN-2.23400040
89143.0100.039.33158.0117.00120.055.5NaN9.035.01.07.5180.599.0NaN25.0NaN8.7113.01.3NaN143.03.82.45.44.5NaNNaN39.514.646.414.9804.0759.027.921NaNNaN-0.0325811258
91084.0100.037.70137.085.0065.023.0NaN0.025.01.07.4243.099.0NaN18.0NaNNaN109.01.1NaN116.01.12.1NaN3.9NaNNaN32.810.929.99.9NaN115.076.7100.01.0-2.36250023

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
203262063494.0100.038.50128.587.069.023.0NaN3.027.00.77.4552.097.0NaN15.0NaNNaN111.00.7NaN148.0NaN2.2NaN4.7NaNNaN32.010.533.68.3NaN226.057.2610.01.0-2.90220020
203272063586.0100.037.72155.0117.079.022.0NaN0.024.0NaN7.4137.0NaNNaN12.0NaN7.8110.00.8NaN118.01.01.82.23.7NaNNaN28.710.145.49.0NaN301.056.751NaNNaN-0.01430042
203282063674.0100.036.89125.083.063.022.0NaN0.024.01.07.3942.094.01033.040.0204.0NaN109.01.5NaN105.0NaN2.3NaN4.51.8NaN30.29.738.36.4NaN248.082.3600.01.00.32470043
2032920637121.0100.037.72196.0148.0100.032.0NaN-5.020.01.07.3246.0NaNNaN76.0NaN9.2102.04.3NaN529.01.02.26.05.7NaNNaN32.510.436.228.2NaN369.060.6611.00.0-0.0214311142
203302063894.0100.037.11168.0141.0137.032.0NaNNaN30.0NaNNaNNaNNaNNaN18.0NaN9.1109.01.3NaN98.0NaN2.13.53.9NaNNaN37.913.424.87.6NaN176.068.381NaNNaN-0.02420041
2033120639120.0100.036.94117.088.0NaN27.0NaNNaN19.0NaNNaNNaNNaN80.032.0154.08.2105.00.8NaN89.0NaN1.64.04.13.1NaN31.19.733.418.1263.040.059.1411.00.0-0.02260026
203322064096.0100.037.55149.089.067.021.0NaN-1.020.01.07.4344.098.0NaN15.0NaN7.7106.00.7NaN272.0NaN3.5NaN5.1NaNNaN36.712.735.414.3NaN162.074.5300.01.0-59.09270025
2033320641101.599.037.39169.097.576.521.0NaNNaN31.0NaNNaNNaNNaNNaN18.0NaN9.0102.00.8NaN121.0NaN2.33.83.6NaNNaN32.911.123.010.2NaN314.033.011NaNNaN-0.01260021
2033420642130.0100.037.06139.097.068.022.0NaN0.029.00.57.4534.099.0NaN12.0NaN8.9107.00.4NaN119.0NaN2.04.54.7NaNNaN32.411.127.48.0NaN267.069.800NaNNaN-10.58420042
2033520643140.0100.038.78205.0135.0105.017.0NaN3.026.00.87.4867.098.0117.026.0150.08.3105.02.5NaN205.01.62.54.83.90.9NaN36.012.533.416.1NaN367.062.291NaNNaN-0.03351133